Mean shift is a popular optimization framework for analyzing the structure of kernel-smoothed function surfaces. The mean shift procedure is an adaptive gradient ascent algorithm with automatic step-size selection and is convergent to a mode of the kernel-smoothed estimate of the function surface. The mean shift framework provides an efficient solution to the general data-clustering problem. See K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, San Diego, 1990; Y. Cheng, Mean shift, mode seeking, and clustering, IEEE Trans. Pattern Anal. Machine Intell., 17(8):790-799, 1995; D. Comaniciu and P. Meer, Mean shift: A robust approach toward feature space analysis, IEEE Trans. Pattern Anal. Machine Intell., 24(5):603-619, 2002.
The mode-seeking property of the mean shift algorithm has been successfully applied to a wide range of vision problems such as tracking and segmentation. See D. Comaniciu, V. Ramesh, and P. Meer, Real-time tracking of non-rigid objects using mean shift, In IEEE Conf. Computer Vision and Pattern Recognition, pages 142-149, 2000; R. T. Collins, Mean-shift blob tracking through scale space, In IEEE Conf. Computer Vision and Pattern Recognition, pages 11:234-240, 2003; D. Comaniciu and P. Meer, Mean shift analysis and applications, In Int. Conf. Computer Vision, pages 1197-1203, 1999; K. Okada, D. Comaniciu, and A. Krishnan, Robust anisotropic Gaussian fitting for volumetric characterization of pulmonary nodules in multislice CT, IEEE Trans. Medical Imaging, 24(3):409-423, 2005. Unfortunately, such formulations have difficulty with hard-to-discover weak modes in multimodal data, for example.